April 2019
Beginner
238 pages
5h 18m
English
Content preview from Design for How People Think
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[ Index ]
A
abstract concepts, 31–37
acuity, visual, 16–17
affinities and psychological profiles, 141–143
AI Winter, 196
analyzing dreams, 55, 59–60, 131–133
analyzing user research. See Post-It note categorization method
analyzing word frequency, 96–97
appeal (form of emotion), 130–131, 161–163, 175
Ariely, Dan, 57
artifacts (what researchers notice), 68
artificial intelligence
artificial neural networks, 197–199
background information, 196–197
recommendations for, 202
statistical learning, 197–199
Assistant app (Google), 198
assumptions
challenging internal, 155–156
contextual interviews and, 71, 81
empathy research and, 65–66
attention. See vision, attention, and automaticity
audience segmentation
case study, Millennial money, 152–154
case study, trust in credit, 154
challenging internal assumptions, 155–156
creating, 77–78, 113, 141, 152–154
emotion and, 147–148
empathy research and, 155–158
finding the dimensions, 152–155
identifying, 117
language and, 143–146
psychographic profiles, 131–132, 141–143
recommendations for, 160
wayfinding and, 149–151
augmented reality (AR), 24
automaticity. See vision, attention, and automaticity
awaken (form of emotion), 130–131, 165–166, 175
B
behaviors, unconscious, 12–14, 64, 70
blockers (problems), 52–53
boundary extension, 35–36
brain
artificial intelligence and, 196–198
spatial information and, 19–21
too much information, 56–57
what information/pathway, 9
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